Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations. Fox, G. & Jha, S. In pages 439-448, 3, 2020. Institute of Electrical and Electronics Engineers (IEEE). ![Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 2 downloads We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.
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abstract = {We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.},
bibtype = {inproceedings},
author = {Fox, Geoffrey and Jha, Shantenu},
doi = {10.1109/escience.2019.00057}
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Downloads: 2
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